{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Light-weight Document Image Cleanup using Perceptual Loss\n",
    "## Abstract\n",
    "Smartphones have enabled effortless capturing and sharing of documents in digital form. The documents, however, often undergo various types of degradation due to aging, stains, or shortcoming of capturing environment such as shadow, non-uniform lighting, etc., which reduces the comprehensibility of the document images. In this work, we consider the problem of document image cleanup on embedded applications such as smartphone apps, which usually have memory, energy, and latency limitations due to the device and/or for best human user experience. We propose a light-weight encoder decoder based convolutional neural network architecture for removing the noisy elements from document images. \n",
    "To compensate for generalization performance with a low network capacity, we incorporate the perceptual loss for knowledge transfer from pre-trained deep CNN network in our loss function. In terms of the number of parameters and product-sum operations, our models are 65-1030 and 3-27 times, respectively, smaller than existing state-of-the-art document enhancement models. Overall, the proposed models offer a favorable resource versus accuracy trade-off and we empirically illustrate the efficacy of our approach on several real-world benchmark datasets.\n",
    "\n",
    "\n",
    "### Cite\n",
    "https://link.springer.com/chapter/10.1007/978-3-030-86334-0_16\n",
    "\n",
    "@InProceedings{10.1007/978-3-030-86334-0_16,\n",
    "author=\"Dey, Soumyadeep\n",
    "and Jawanpuria, Pratik\",\n",
    "editor=\"Llad{\\'o}s, Josep\n",
    "and Lopresti, Daniel\n",
    "and Uchida, Seiichi\",\n",
    "title=\"Light-Weight Document Image Cleanup Using Perceptual Loss\",\n",
    "booktitle=\"Document Analysis and Recognition -- ICDAR 2021\",\n",
    "year=\"2021\",\n",
    "publisher=\"Springer International Publishing\",\n",
    "address=\"Cham\",\n",
    "pages=\"238--253\",\n",
    "isbn=\"978-3-030-86334-0\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "import sys\n",
    "import ssl\n",
    "from matplotlib import pyplot as plt\n",
    "ssl._create_default_https_context = ssl._create_unverified_context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from train import train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_folder = 'sample_data'\n",
    "gt_folder = 'sample_gt_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 256\n",
      "sample_data\n",
      "sample_gt_data\n",
      "sample_data\n",
      "Generating training blocks!!!\n",
      "dataset/sample_data\n",
      "dataset/sample_data\n",
      "['image_56.png', 'image_42.png']\n",
      "image_56.png\n",
      "dataset/sample_gt_data/image_56.png\n",
      "dataset/sample_data/image_56.png\n",
      "(1613, 1493, 3) (1613, 1493, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|█████     | 1/2 [00:01<00:01,  1.96s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image_42.png\n",
      "dataset/sample_gt_data/image_42.png\n",
      "dataset/sample_data/image_42.png\n",
      "(1308, 2379, 3) (1308, 2379, 3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:04<00:00,  2.46s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of training blocks generated:  496\n",
      "tf.Tensor(394516500.0, shape=(), dtype=float32)\n",
      "checkpoints/M16_gray.json\n",
      "396/396 [==============================] - ETA: 0s - loss: 208049664.0000\n",
      "Epoch 00001: val_loss improved from inf to 112988376.00000, saving model to checkpoints/M16_gray_sample_data_epoch-01.hdf5\n",
      "396/396 [==============================] - 1369s 3s/step - loss: 208049664.0000 - val_loss: 112988376.0000\n"
     ]
    }
   ],
   "source": [
    "#train(data_folder,gt_folder,dataset_path='dataset',checkpoint='checkpoints',epochs=10,pretrain_flag=False,pretrain_model_weight_path=None,model_name='M32',gray_flag=True,block_size=(256,256),train_batch_size=1)\n",
    "#model_name = name of the model (M16,M32,M64)\n",
    "#dataset_path==> path to the training dataset\n",
    "#checkpoint==> Path where saved models will be saved\n",
    "#pretrain_flag=True if you are initializing your model with some pre-trained weight\n",
    "#pretrain_model_weight_path==> path to the pretrained model weight (if pre-trained model flag is set to True)\n",
    "#gray_flag=True if you want final layer of the model to have only 1 channel\n",
    "#in case of color output, we need to set gray_flag=False (by default it is set as True)\n",
    "#block_size==> patch size per image/model input size, (by default=(256 X 256))\n",
    "#train_batch_size==> bath size during training\n",
    "\n",
    "model_name = 'M16'\n",
    "train(data_folder,gt_folder,dataset_path='dataset',checkpoint='checkpoints',epochs=1,gray_flag=True,model_name=model_name,pretrain_flag=True,pretrain_model_weight_path='checkpoints/M16_dibco13_epoch-958.hdf5')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference per image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from infer import infer_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"M16Gray\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 256, 256, 16) 448         input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 256, 256, 16) 64          conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 256, 256, 16) 64          conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 256, 256, 16) 64          conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 256, 256, 16) 0           batch_normalization_15[0][0]     \n",
      "                                                                 batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 256, 256, 16) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 256, 256, 16) 2320        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 256, 256, 16) 64          conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 256, 256, 16) 64          conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 256, 256, 16) 0           batch_normalization_17[0][0]     \n",
      "                                                                 activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 256, 256, 16) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 256, 256, 16) 2320        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 256, 256, 16) 64          conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 256, 256, 16) 64          conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 256, 256, 16) 0           batch_normalization_19[0][0]     \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 256, 256, 16) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 256, 256, 16) 2320        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 256, 256, 16) 64          conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 256, 256, 16) 64          conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 256, 256, 16) 0           batch_normalization_21[0][0]     \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 256, 256, 16) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 256, 256, 16) 2320        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 256, 256, 16) 64          conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 256, 256, 16) 64          conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 256, 256, 16) 0           batch_normalization_23[0][0]     \n",
      "                                                                 activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 256, 256, 16) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 256, 256, 16) 0           batch_normalization_13[0][0]     \n",
      "                                                                 activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 256, 256, 16) 2320        add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 256, 256, 16) 64          conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 256, 256, 1)  145         batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 256, 256, 1)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 256, 256, 1)  4           conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 256, 256, 1)  0           lambda[0][0]                     \n",
      "                                                                 batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sigmoid_1 (TensorFl (None, 256, 256, 1)  0           add_13[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 26,885\n",
      "Trainable params: 26,499\n",
      "Non-trainable params: 386\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "##########################################################################################\n",
    "#Input image\n",
    "test_img_name = 'dataset/sample_data/image_56.png'\n",
    "##########################################################################################\n",
    "#calling inference engine\n",
    "out_img_name = 'test_out1.jpeg'\n",
    "infer_image('checkpoints/M16_gray.json','checkpoints/M16_gray_sample_data_epoch-01.hdf5',test_img_name,out_img_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##########################################################################################\n",
    "#Dispaly input output image\n",
    "\n",
    "in_img = cv2.imread(test_img_name,1)\n",
    "plt.imshow(in_img,cmap = 'gray')\n",
    "plt.title('Input')\n",
    "plt.show()\n",
    "\n",
    "out_img = cv2.imread(out_img_name,1)\n",
    "plt.imshow(out_img,cmap = 'gray')\n",
    "plt.title('M16 output')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# inference per folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from infer import infer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"M16Gray\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 256, 256, 16) 448         input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 256, 256, 16) 64          conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 256, 256, 16) 64          conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 256, 256, 16) 64          conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 256, 256, 16) 0           batch_normalization_15[0][0]     \n",
      "                                                                 batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 256, 256, 16) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 256, 256, 16) 2320        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 256, 256, 16) 64          conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 256, 256, 16) 64          conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 256, 256, 16) 0           batch_normalization_17[0][0]     \n",
      "                                                                 activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 256, 256, 16) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 256, 256, 16) 2320        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 256, 256, 16) 64          conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 256, 256, 16) 64          conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 256, 256, 16) 0           batch_normalization_19[0][0]     \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 256, 256, 16) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 256, 256, 16) 2320        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 256, 256, 16) 64          conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 256, 256, 16) 64          conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 256, 256, 16) 0           batch_normalization_21[0][0]     \n",
      "                                                                 activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 256, 256, 16) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 256, 256, 16) 2320        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 256, 256, 16) 64          conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 256, 256, 16) 2320        batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 256, 256, 16) 64          conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 256, 256, 16) 0           batch_normalization_23[0][0]     \n",
      "                                                                 activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 256, 256, 16) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 256, 256, 16) 0           batch_normalization_13[0][0]     \n",
      "                                                                 activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 256, 256, 16) 2320        add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 256, 256, 16) 64          conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 256, 256, 1)  145         batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 256, 256, 1)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 256, 256, 1)  4           conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 256, 256, 1)  0           lambda[0][0]                     \n",
      "                                                                 batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "tf_op_layer_Sigmoid_1 (TensorFl (None, 256, 256, 1)  0           add_13[0][0]                     \n",
      "==================================================================================================\n",
      "Total params: 26,885\n",
      "Trainable params: 26,499\n",
      "Non-trainable params: 386\n",
      "__________________________________________________________________________________________________\n",
      "dataset/sample_data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:31<00:00, 15.73s/it]\n"
     ]
    }
   ],
   "source": [
    "input_dir = 'dataset/sample_data'\n",
    "out_dir = 'sample_out_data'\n",
    "infer('checkpoints/M16_gray.json','checkpoints/M16_gray_sample_data_epoch-01.hdf5',input_dir,out_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
